JUUL from the USA to Indonesia: implications for expansion to LMICs
Bibliographic record
Abstract
Indonesia has one of the largest tobacco markets in the world, well known for their clove cigarettes, kreteks . However, electronic cigarettes (e-cigarettes) are growing in popularity among Indonesians. While conventional cigarettes are sold in stores and kiosks,1 e-cigarettes are sold online (35.3%) and through vape shops (64.7%).2 The 2011 Indonesian Global Adult Tobacco Survey, the latest available national data, reported awareness of e-cigarettes was 10.9% and current use was 2.5%.3 Recent social media and sales data imply that e-cigarette use has grown since then. Indonesia has the second largest share of Instagram posts about vaping of any country,4 and e-cigarette sales reached 2.1 trillion rupiah (US$144.5 million) in 2018. Total sales are forecasted to reach 6.1 trillion rupiah (IDR) (US$419.6 million) by 2022.2 JUUL is the leading e-cigarette brand in the United States of America (USA), with 72% of the vapour product market share as of August 2018.5 According to their website, JUUL sells their products in the USA, Canada, Israel, UK, Italy, Germany, Switzerland, France and Russia.6 However, these products are also reported to be sold in low-income and middle-income countries (LMICs) like Indonesia, where interest in vaping and JUUL (as measured by Google Trends) has increased from …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".